In this paper we will leverage the significant work that has been made in the field of Fairness, and study how it can be extrapolated to industrial computer vision applications.
Fairness in Machine Learning considers the relationships between an algorithm and a certain input variable that should not play any role in the model's decision from an ethical, legal or technical point of view, but has a considerable influence on the system's behavior nonetheless.
This variable is usually called the sensitive variable. Different definitions have been put in the statistical literature, each of them considering specific dependencies between the sensitive variable and the decision algorithm. From a more practical point of view, Fairness issues in Machine Learning manifest themselves in the shape of undesired algorithmic biases in the model's predictions, such as according more bank mortgages to males than females for similar profiles or hiring males rather than females for some specific job profiles, due to a majority presence of male individuals with the corresponding profile in the learning database. Hence, Fairness initially gained a lot of attention specifically in social applications, with a large amount of articles speaking out about the different types of bias that ML algorithms amplify. We refer for instance to the recent review papers of [1]}, [2]} and references therein.
